Should I stop doing SEO and do AEO instead?

No. Conventional search still drives most organic traffic for most sites, and AI engines rely heavily on the same signals. Treat AEO as an additional surface layered onto a healthy SEO programme, not a replacement for one.

Do keywords still matter for AEO?

Keyword intent matters; keyword density does not. AI engines match on meaning rather than exact strings, so the useful work is covering the question a buyer is actually asking, in the words they would use, rather than repeating a target phrase.

Does backlink building help AEO?

Indirectly and substantially. Links remain a strong authority signal, and authority influences which candidate passage a model prefers. But corroboration matters more than raw volume: being independently confirmed by several credible sources beats a larger number of low-quality links.

Which matters more, AEO or SEO?

For nearly every site today, SEO still delivers more traffic. AEO delivers a rising share of high-intent traffic, because someone asking an assistant for a recommendation is usually further along the buying journey than someone typing a broad query.

AEO vs SEO: What Actually Changes

The honest accounting

AEO discourse tends toward two unhelpful extremes: that nothing has changed and it is all SEO with a new label, or that everything has changed and your existing programme is obsolete. Neither is true, and the useful version is more specific.

Here is the split as we see it.

What transfers unchanged

Crawlability and indexability. AI engines have to fetch your pages. The same clean architecture, sensible internal linking, fast responses and server-rendered HTML that serve Googlebot serve GPTBot and ClaudeBot. This is the largest single area of overlap.

Content quality and depth. Retrieval systems are reasonably good at telling whether a page answers a question. Thin content underperforms in both worlds.

Domain authority. Established, widely-referenced domains are preferred sources. The mechanism differs from PageRank, but the effect rhymes.

Structured data. Schema markup was always about making meaning machine-readable. That objective becomes more valuable, not less, when the machine is a language model assembling an answer.

Site performance. Slow pages get abandoned by crawlers with finite budgets. Unchanged.

Topical authority. Covering a subject comprehensively, with pages that interlink coherently, signals expertise to both systems.

What breaks

Position tracking as a KPI. There is no position. An answer either cites you or it does not, and "average position 4.2" has no meaning when the result is a paragraph. Teams that report on rank movements need a genuinely different dashboard, not a renamed column.

Click-through rate optimisation. Title and meta description craft exists to win the click from a list. In a generative answer there is frequently no click at all: the user gets what they needed and stops. Optimising snippets for a surface that does not display them is wasted effort.

The thin long tail. The old model tolerated hundreds of lightly-differentiated pages each earning a small number of visits. Answer engines consolidate: one strong page covering a topic properly will be retrieved, and the twelve near-duplicates around it will not. Consolidation now beats proliferation.

Exact-match keyword targeting. Models match semantically. Building a separate page for each phrasing variant of the same question produces internal competition rather than coverage.

Ranking-only reporting cadence. Conventional rank data is stable enough to check weekly. AI answers vary between identical queries, so a single weekly observation is noise. Measurement has to become statistical.

What is genuinely new

Brand representation

This is the real addition, and it has no SEO analogue.

When someone asks an assistant to compare options in your category, the model describes you from everything it has absorbed. It is not quoting a page you control. It is synthesising, and it can synthesise a version of you that is outdated, incomplete, or wrong: a discontinued pricing model, a feature you shipped two years ago described as missing, a limitation that was fixed in a release the model never saw.

There is no rank to check here. The only way to know is to ask the questions your buyers ask and read the answers, repeatedly, across engines. This is why AEO tooling looks more like brand monitoring than like a rank tracker.

Multi-engine divergence

Google was effectively one target. Answer engines are several, they disagree, and they disagree systematically. Perplexity leans heavily on recent web sources and cites generously. ChatGPT blends training knowledge with retrieval and cites more sparingly. Gemini is tied into Google's index. Claude is conservative about unverified claims.

The consequence is that AEO performance is per-engine. Being strong in Perplexity and absent from ChatGPT is a common and specific problem, and it needs a specific diagnosis rather than a general "improve visibility" push.

Answer-shaped structure

The answer-first pattern, question-phrased headings, self-contained passages that survive being lifted out of context. Good writers did some of this instinctively. AEO makes it a requirement rather than a stylistic preference, because the unit of retrieval is a passage rather than a page.

Agent usability

The newest and least-discussed area. Increasingly, the thing arriving at your site is not a human and not an indexing crawler but an agent trying to complete a task on a user's behalf: find pricing, check availability, start a trial.

Agents fail on things that never bothered a human tester. A price behind a hover state. A comparison table rendered as an image. A trial signup requiring three JavaScript-dependent steps. None of that shows up in a conventional audit, and all of it stops an agent from getting the answer, which means your brand is reported as unavailable or unclear.

Only a small number of tools test this at all. CiteCue's Agent Usability runs an agent through real tasks and reports the step where it stalls, which is the only reliable way we know to find these failures before a buyer does.

How to allocate effort

A defensible split for a team with an existing, healthy SEO programme:

Keep doing technical hygiene, content depth, authority building. This is shared infrastructure and it serves both surfaces.
Stop doing thin page proliferation, exact-match variant pages, and CTR micro-optimisation on queries that increasingly resolve without a click.
Start doing brand representation monitoring, per-engine diagnosis, answer-first restructuring of your highest-value pages, and agent task testing.
Change how you report. Replace average position with citation rate and share of voice, sampled properly. See The AEO Metrics That Matter.

The reason this reallocation is usually affordable is that item 2 frees up real capacity. Most content programmes carry a large volume of pages that no longer earn their keep in either world.

The uncomfortable conclusion

If your SEO programme is weak, AEO will not rescue it. The signals are too entangled: an unauthoritative domain with unreachable pages and thin content will fail in generative answers for the same underlying reasons it fails in ranked results.

AEO is best understood as a demanding extension of a healthy search programme. Teams that treat it as an escape from SEO fundamentals tend to discover, six months in, that they have rebuilt the fundamentals anyway.